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News from NVIDIA GPU Technology Conference, GTC 2024: CEO Introduces Blackwell Artificial Intelligence (AI) Processor, Announces Alliances with GE Healthcare, J&J MedTech

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March 19, 2024 — The Imaging Technology News (ITN) editorial team is tuned into NVIDIA GTC 2024 (GPU Technology Conference) which, during a keynote address Monday, offered a “transformative moment in AI” when NVIDIA CEO Jensen Huang introduced the Blackwell AI processor among dozens of product and platform developments and partnerships toward what he called “a new industrial revolution.” Huang addressed thousands of industry leaders, developers, researchers and business strategists in discussing innovations, like the Blackwell platform which, the company announced, powers a new era of computing and generative AI with unparalleled performance, efficiency, and scale.

At ITN, we’ve covered AI, machine learning (ML), deep learning (DL) and those medical imaging/radiology leaders leveraging the technology tools over the past several years, and on a regular basis share news and newsmakers. During NVIDIA GTC 2024, we’re excited to follow, learn and share AI and Deep Learning (DL) visionaries whose work has impacted healthcare, radiology and patient care, including representatives from Stanford University’s Human Centered Artificial Intelligence (HAI), such as its Co-Director, Fei Fei Le, among others.

For now, we thought it important to share coverage of NVIDIA news from some of the key players which made headlines, as well as related coverage ITN has provided, including NVIDIA’s news in December 2023. In summary, at that time, NVIDIA launched a cloud service for medical imaging AI to further streamline and accelerate the creation of ground-truth data and training of specialized AI models through fully managed, cloud-based application programming interfaces. In so doing, it reported that NVIDIA MONAI cloud APIs — announced at the annual meeting of the Radiological Society of North America, RSNA 2023 — provide an expedited path for developers and platform providers to integrate AI into their medical imaging offerings using pretrained foundation models and AI workflows for enterprises. The APIs are built on the open-source MONAI project founded by NVIDIA and King’s College London.

The company reports that more than 1.8 million developers have downloaded the MONAI framework for AI in medical imaging.

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Additional related coverage we’ve provided recently, linked below, includes the in-depth One on One with Radiological Society of North America (RSNA) President Curtis Langlotz, MD, PhD, of Stanford University, among others which emerged from the Health Information Management Systems Society Global Conference & Exhibition, HIMSS 2024, held last week, the Annual Meeting of the American Society for Radiation OncologyASTRO 2023 and RSNA 2023, as well as vendor and institutional updates. Keeping you informed of industry news and global newsmakers is what we’re all about, and we hope you stay engaged as the world of radiology and the impact of artificial intelligence, deep learning and machine learning — and the growth of large language models for healthcare — moving forward.

VIDEO: One on One with Curtis P. Langlotz, MD, PhD, RSNA President

What follows are the written statements released by GE Healthcare and J&J MedTech from day one of the March 18-21 Conference being held in San Jose, CA.

GE HealthCare Reports on Accelerates AI Innovation with Healthcare-Specific Foundation Models Powered by NVIDIA

Building on a long-term artificial intelligence (AI) collaboration, GE HealthCare used NVIDIA technology to develop its recent research model SonoSAM Track1, which combines a promptable foundation model for segmenting objects on ultrasound images called SonoSAM1. SonoSAM Track focuses on segmenting anatomies, lesions, and other essential areas in ultrasound images, and SonoSAM Lite is a streamlined version of SonoSAM Track, according to a written statement released March 18 by GE Healthcare.

“GE HealthCare is committed to investing in innovative technologies that help tackle some of the industry’s biggest challenges.  Our vision is to accelerate advancements in medical imaging by introducing foundational AI technologies, thereby empowering data scientists to expedite AI application development and eventually help clinicians and enhance patient care. By utilizing these versatile, generalist models, we aim to adapt more efficiently to new tasks and medical imaging modalities, often requiring far less labeled data compared to the traditional model retraining approach. This is particularly significant in the healthcare domain, for which data is especially time-consuming and costly to obtain,” said Parminder Bhatia, Chief AI Officer, GE HealthCare.

In healthcare, leveraging AI to enhance patient care, streamline operational efficiencies, and make informed decisions has become increasingly important. Traditionally, the approach to integrating AI into healthcare systems required the retraining of models to accommodate the unique requirements of different patient populations and hospital settings. This conventional method can lead to heightened costs, complexity, and the need for specialized personnel, therefore hindering the broad adoption of AI technologies in healthcare domains. Foundation models have risen to prominence due to their ability to operate as human-in-the-loop AI systems, garnering significant attention.

Foundation and generative AI models could play a crucial role by enabling swift adaptation to various diseases, facilitating screening, early detection, tracking progression, and identifying non-invasive biomarkers with minimal training requirements, such as zero-shot or few-shot settings. In a recent study conducted by GE HealthCare, its research project, SonoSAMTrack, showcased high performance across seven ultrasound datasets, encompassing a wide range of anatomies (adult heart and fetal head) and pathologies (breast lesions and musculoskeletal pathologies), as well as different scanning devices. Notably, it outperformed competing methods by a substantial margin. In addition, SonoSamTrack exhibited enhanced performance metrics in terms of speed and efficiency, requiring only 2-6 clicks for precise segmentation, thus minimizing user input2. This achievement was made possible through distillation and quantization techniques, utilizing the NVIDIA TensorRT software development kit and other capabilities for quantization-aware training.

“Combining NVIDIA’s accelerated computing and AI technology stack with GE HealthCare’s medical imaging expertise will help enhance patient care by making ultrasound diagnostics quicker and more accurate,” said David Niewolny, Director of Business Development for Healthcare and Medical, NVIDIA. “This collaboration underscores the importance of using AI for life-saving advancements and setting new standards in healthcare.”

1 Technology in development that represents ongoing research and development efforts. These technologies are not products and may never become products. Not for sale. Not cleared or approved by the U.S. FDA or any other global regulator for commercial availability.

2 Hariharan Ravishankar, Rohan Patil, Vikram Melapudi, Harsh Suthar, Stephan Anzengruber, Parminder Bhatia, Kass-Hout Taha, Pavan Annangi. SonoSAMTrack — Segment and Track Anything on Ultrasound Images.

Johnson & Johnson MedTech Announces Its Work with NVIDIA to Scale AI for Surgery

On the first day of the NVIDIA AI conference, Johnson & Johnson MedTech announced it is working to accelerate and scale artificial intelligence (AI) for surgery with NVIDIA, supporting increased access to real-time analysis and global availability of AI algorithms for surgical decision-making, education, and collaboration across the connected operating room (OR).

According to the written statement, the companies executed a memorandum of understanding to accelerate AI for Johnson & Johnson MedTech’s extensive surgical technologies portfolio with NVIDIA’s AI platform for healthcare. The technologies are designed to allow for fast, secure, and real-time AI deployment through Johnson & Johnson MedTech’s digital surgery ecosystem.

“Johnson & Johnson MedTech is advancing healthcare toward a future that’s more connected and personalized,” said Tim Schmid, Executive Vice President, and Worldwide Chairman, MedTech. “This future will be increasingly enabled by digital technologies that deliver efficiency, inform decision-making, and extend surgical training and education. Our deep heritage in healthcare and digital ecosystem in surgery and NVIDIA’s AI platforms hold enormous potential to create a more connected surgical experience.”

A key element of accelerating AI for surgery is advanced edge computing to allow for localized data processing within the OR, a necessary step for AI algorithms to analyze live and stored surgical data in real time. The approach can also help reduce the need for the transfer of sensitive data and allow for specific applications to run separately within the secure computing environment, delivering ultra-low latency within the operating room, where every second matters.

The NVIDIA IGX edge computing platform and the NVIDIA Holoscan edge AI platform create infrastructure to deploy AI-powered software applications in the OR.

Johnson & Johnson MedTech will also leverage AI technologies to extend its open ecosystem for surgery. NVIDIA’s purpose-built solutions are designed to help accelerate innovation throughout the ecosystem and speed the development and deployment of AI-powered applications in a secure and scalable manner.

“One of the major challenges in scaling AI for surgery is the closed design of surgical technologies,” said Shan Jegatheeswaran, Vice President and Global Head of Digital, MedTech. He added, “Bringing advanced edge computing hardware and software to the OR enables scalability of innovation and new AI-powered solutions for clinical decision-making, education and training, and collaboration – with the ultimate goal of advancing patient care.”

Follow Imaging Technology News as our editorial team will continue to monitor and report on healthcare related news emerging from the NVIDIA GPU Technology Conference, GTC 2024.

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